1. Field of the Invention
This invention relates to detection of abnormal anatomical regions in radiographs, and, more particularly, to the detection of masses and microcalcifications in digital mammograms.
2. Background
Detection and diagnosis of abnormal anatomical regions in radiographs, such as masses and microcalcifications in womens' breast radiographs, so-called mammograms, are among the most important and difficult tasks performed by radiologists.
Microcalcifications are small-sized (25 micrometers to a few millimeters) calceous formations.
Breast cancer is a leading cause of premature death in women over forty years old. There is a great deal of evidence to show that early detection, diagnosis and treatment of breast cancer significantly improves the chances of survival, reducing breast cancer morbidity and mortality. Many methods for early detection of breast cancer have been studied and tested, among them mammography. To date X-ray mammography has proven the most effective means of providing useful information to diagnosticians regarding abnormal features in the breast and potential risks of developing breast cancer (Detection of Breast Cancer, Strax, P. Cancer 1990, Vol. 66, pp. 1336-1340). The American Cancer Society currently recommends the use of mammography for screening of asymptomatic women over the age of forty with annual examinations after the age of fifty. Mammography will eventually constitute one of the highest volume X-ray procedures routinely interpreted by radiologists.
Between thirty and fifty percent of breast carcinomas detected radiographically demonstrate microcalcifications on mammograms, and between sixty and eighty percent of breast carcinomas reveal microcalcifications upon microscopic examination. Therefore, any increase in the detection of microcalcifications by mammography may lead to further improvements in its efficiency in the detection of early breast cancer.
Currently acceptable standards of clinical care are that biopsies are performed on five to ten women for each cancer removed. With this high biopsy rate is the reasonable assurance that most mammographically detectable early carcinomas will be resected. Given the large amount of overlap between the characteristics of benign and malignant lesions on mammograms, computer-aided detection of abnormalities will have a great impact in clinical care.
At present, mammogram readings are performed visually by mammograplnic experts, that is, physicians and radiologists. Unfortunately, visual reading of mammograms has two major disadvantages. First, it is often possible to miss the breast cancer in its early stages. This is because, unlike many other cancers, there is as yet no way to detect premalignant changes in the breast. This results partly from the relative inaccessibility of breast tissue. A second disadvantage of visual reading of mammograms is that these readings are both labor intensive and time consuming. Multiple readings of a single mammogram may be necessary in order to increase the reliability of the diagnosis.
Therefore, it would be advantageous and useful to have computer-assisted or aided detection (CAD) systems to help radiologists and physicians obtain quicker and more precise results when analyzing mammograms. Such CAD systems would aid in cancer detection and improve the efficiency of large-scale screening.
Various computer assisted detection systems have been investigated to assist diagnosticians in their diagnosis of breast cancer. While some of these systems tend to be overly conservative, thereby having a higher false-positive detection ratio, studies show that computer assisted detection systems can provide a useful "second opinion" to diagnosticians, and, under the help and guidance of computer assisted detection systems, the accuracy of diagnosis of mammography has improved.
Systems that alert a diagnostician to the location of possible breast masses and microcalcifications should reduce the number of false positive and false negative diagnoses, which, in turn, should lead to earlier detection of primary breast cancers and a better prognosis for the patients.
A mass shown in a mammogram is a very important sign in the diagnosis of breast cancer, and therefore, computerized detection of masses in digitized mammograms is basic to any computer assisted detection system. However, because masses in digitized mammograms demonstrate a large variation in their image features and because they can be obscured by normal breast parenchyma in the mammogram, the computer assisted detection of masses in mammograms is very difficult.
A typical system of computerized mass detection in mammograms involves two steps (or stages), namely image segmentation and image feature analysis. Some computer assisted detection systems use single-image segmentation in their first or image segmentation stage in order to search for suspicious regions of masses by analyzing a single input image (see, for example, An approach to automated detection of tumors in mammograms, Brzakovic, D., et al., IEEE Trans. Med. Imaging, Vol. 9(3), pp. 233-241, 1990; and On techniques for detecting circumscribed masses in mammograms, Lai, S. M., et al. IEEE Trans. Med. Imaging, Vol. 8, pp. 377-386, 1989). Other computer assisted detection systems use bilateral-image subtraction in their first or segmentation stage in order to select possible mass regions based on the asymmetry in left and right image pairs (see, for example, Investigation of methods for the computerized detection and analysis of mammographic masses, Giger, M. L., et al., Proc. SPIE, 1233, pp. 183-184, 1990; and Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images, Yin, F. F., et al., Med. Phys., Vol. 18, pp. 955-983, 1991) .
Having performed image segmentation in order to obtain an over-inclusive list of suspected regions in the input image, different feature analyses are then applied to the image in the suspected regions in order to distinguish positive or negative masses (see, for example, Computerized detection of masses in digital mammograms: investigation of feature-analysis techniques, Yin, F. F., et al., J. of Digital Imaging, Vol. 7, pp. 218-237, 1994).
A single-image segmentation technique uses a local thresholding algorithm to process the image, and as a result, it usually selects more suspicious areas than a bilateral-image subtraction technique. Without an effective method of feature analysis in the second stage, however, the computer assisted detection system will produce a high rate of false-positive detection. Thus, using single-image segmentation, current techniques are usually limited to detect particular types of masses, namely those which show certain well defined characteristics, such as uniform density inside an area, an approximately circular shape of varying size, and fuzzy edges (see Automated detection and classification of breast tumors, Ng, S. L., et al., Computers & Biomedical Research, Vol. 25, pp. 218-237, 1992; and Computer detection of stellate lesions in mammograms, Kegelmeyer, W. P., Proc. SPIE, 1660, pp. 446-454, 1992).
Techniques of alignment and subtraction of bilateral-images reduce the number of possible mass regions selected in the first stage of a computer assisted detection. Computer assisted detection systems using bilateral-image subtraction have been tested to detect wide varieties of mass in digitized mammograms. (Yin, et al., Comparison of bilateral-subtraction and single-image processing techniques in the computerized detection of mammographic masses, Invest. Radiol., Vol. 6, pp. 473-481, 1993), show that because bilateral-image subtraction uses asymmetry in the left-right image pair, it produces better results than single-image segmentation in computerized mass detection systems. However, bilateral-image subtraction has drawbacks, including that a left-right image pair of mammograms is not always available. Further, even when a pair of mammograms is available, it is sometimes difficult to align the images in them. Alignment involves matching angles, positions, size, etc.
Microcalcifications are ideal targets for computer-aided detection schemes because of their clinical relevance, their potential subtlety, and, until reaching the actual noise level in the image, the lack of coexisting normal structures with similar appearance (Vyborny, C. J., et al. Computer vision and artificial intelligence in mammography, AJR 1994; 162:699-708.). Several CAD methods have been developed to detect clustered microcalcifications. The first step of these methods is to segment possible microcalcifications within the image from the normal tissue and image noise. Different methods, such as image-subtraction (Chan, H. P., et al., Image feature analysis and computer-aided diagnosis in digital radiography: 1. Automated detection of microcalcifications in mammography, Med. Phys 1987; 14:538-548; and Chan, H. P. et al, Computer-aided detection of microcalcifications in mammograms: methodology and preliminary clinical study, Invest Radiol 1988; 23:664-671), local area thresholding (Davies, D. H., et al., Automatic computer detection of clustered calcifications in digital mammograms, Phys Med Biol, 1990; 35:1111-1118; and Davies, D. H., et al. The automatic computer detection of subtle calcifications in radiographically dense breasts, Phys Med Biol, 1992; 37:1385-1390), and pixel-based feature testing (Fam, B. W., et al., Algorithm for the detection of fine clustered calcifications on film mammograms, Radiology, 1988; 169:333-337) are used for this purpose. If segmentation is implemented with high sensitivity (i.e., to include as many true microcalcifications as possible), most approaches routinely yield a large number of suspected microcalcifications. Therefore, a feature analysis method is followed to classify actual positive or negative detection. Many features, such as area, mean gray level, gray level deviation, contrast, shape factor, and edge strength (Shen, L., et al., Application of shape analysis to mammographic calcifications, IEEE Trans. Imaging, 1994; 13:263-274.11; Zhao, D., Rule-based morphological feature extraction of microcalcifications in mammograms, Proc. SPIE, 1993; 1905:702-715; and Woods, K.S., et al., Comparative evaluation of pattern recognition techniques for detection of microcalcifications, Proc. SPIE, 1993; 1905:841-852) have been used for this feature analysis, with varying degrees of success.
The difficulty in attaining simultaneously both high sensitivity and specificity in the detection of clustered microcalcifications needed to be clinically useful has attracted a significant research effort.
Some research has focused on yielding high sensitivity by increasing the quality of digitization (Chan, H. P., Digitization requirements in mammography: Effects on computer-aided detection of microcalcifications, Med. Phys., 1994; 21:1203-1210). Other research has focused on reducing the rate of false-positive detection, through the use of morphological filters (Nishikawa, R. M., et al., Use of morphological filters in the computerized detection of microcalcifications in digital mammograms, (abstr), Med. Phys. 1990; 17:524), different clustering methods (Nishikawa, R. M., et al., Computer-aided detection of clustered microcalcifications: An improved method for grouping detected signals, Med. Phys. 1993; 20(6):1661-1666), and artificial neural networks (Stafford, R. G., et al., Application of neural networks to computer-aided pathology detection in mammography, Proc. SPIE, 1993; 1896:341-352; Wu, Y., et al., Computerized detection of clustered microcalcifications in digital mammograms: Applications of artificial neural networks, Med. Phys. 1992; 19:555-560; and Zhang, W., et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network, Med. Phys., 1994; 21:517-524). Using these techniques, the false positive detection rate can be reduced to below 1.5 clusters per image in limited image sets (Nishikawa, R. M., et al., Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms, Proc. SPIE, 1993; 1905:422-432).
To date, research in computerized detection systems for masses and microcalcifications in digital mammograms has largely been based on thresholdling methods or neural networks. For example, U.S. Pat. No. 5,331,550 uses neural networks as an aid in medical diagnosis and general anomaly detection.
Doi, U.S. Pat. No. 4,907,156, uses varying threshold levels to detect nodules for enhancement and detection of abnormal anatomic regions in a digital image. The difficulties of detection of such regions is noted by Doi, who states that "several investigators have attempted to analyze mammographic abnormalities with digital computers. However, the known-studies failed to achieve an accuracy acceptable for clinical practice." (Col. 2, lines 24-27) U.S. Pat. No. 5,289,374 discloses a method and system for analysis of false positive produced by the system of Doi '156 to reduce the false positive found by the latter system.
Giger et al, U.S. Pat. No. 5,133,020, uses a thresholding technique to locate abnormal regions in a digital image of a radiograph, and then uses classification processing to determine whether the detected abnormal region is benign or malignant. Giger's classification is based on the degree of spiculation of the identified abnormal regions.
U.S. Pat. No. 5,319,549 uses texture analysis on a number of small regions of interest in order to determine a classification of normal or abnormal of a patient's lungs.
Doi, U.S. Pat. No. 5,343,390, discloses a method and system for automated selected of regions of interest and detection of septal lines in digital chest radiographs. Doi uses a large number of adjacent regions of interest selected corresponding to an area on a digital image of a patients lungs. The regions of interest each contain a number of square or rectangular pixel arrays and are selected to sequentially fill in the total selected area of the lungs to be analyzed. The regions of interest are analyzed to determine those exhibiting sharp edges, that is, high edge gradients. A percentage of the sharp edge regions of interest are removed from the original sample based on the edge gradient analysis, the majority of which correspond to rib edge containing regions of interest. After removal of the sharp edge regions of interest, texture measurements are taken on the remaining sample in order to compare such data with predetermined for normal and abnormal lungs.
The above methods and systems have various disadvantages and deficiencies, including that they use absolute measurements only, they are not as robust as image quality variations, and either their sensitivity is too low or they tend to result in too many false positives.
Although a computer assisted detection system using bilateral-image subtraction can improve the performance of detection by reducing the rate of false-positive detections, its sensitivity and versatility is also decreased. These inventors' studies have found that improved methods of single-image segmentation can achieve a higher detection sensitivity and will be more versatile in future clinical applications. Unfortunately, because of the lack of effective methods of feature analysis, many computer assisted detection systems using single-image segmentation have high rates of false-positive detection if they select more suspicious mass regions.
It is therefore useful and necessary to balance the high detection sensitivity of computer assisted detection systems with true-positive and low rates of false-positive detection.